FEVD_Plots {BHSBVAR}

Plot Forecast Error Variance Decompositions

Description

Plot Forecast Error Variance Decompositions

Usage

FEVD_Plots(
  results,
  varnames,
  shocknames = NULL,
  xlab = NULL,
  ylab = NULL,
  rel = TRUE
)

Arguments

results

List containing the results from running BH_SBVAR().

varnames

Character vector containing the names of the endogenous variables.

shocknames

Character vector containing the names of the shocks.

xlab

Character label for the horizontal axis of impulse response plots (default = NULL). Default produces plots without a label for the horizontal axis.

ylab

Character label for the vertical axis of impulse response plots (default = NULL). Default produces plots without a label for the vertical axis.

rel

Boolean indicating whether to display forecast error variance explained by the shock as a percent of total forecast error variance (default = TRUE).

Details

Plots forecast error variance decompositions and returns a list containing the actual processed data used to create the plots.

Value

A list containing forecast error variance decompositions.

Author(s)

Paul Richardson

Examples

# Import data
library(BHSBVAR)
set.seed(123)
data(USLMData)
y0 <- matrix(data = c(USLMData$Wage, USLMData$Employment), ncol = 2)
y <- y0 - (matrix(data = 1, nrow = nrow(y0), ncol = ncol(y0)) %*% 
             diag(x = colMeans(x = y0, na.rm = FALSE, dims = 1)))
colnames(y) <- c("Wage", "Employment")

# Set function arguments
nlags <- 8
itr <- 5000
burn <- 0
thin <- 1
acc <- TRUE
h <- 20
cri <- 0.95

# Priors for A
pA <- array(data = NA, dim = c(2, 2, 8))
pA[, , 1] <- c(0, NA, 0, NA)
pA[, , 2] <- c(1, NA, -1, NA)
pA[, , 3] <- c(0.6, 1, -0.6, 1)
pA[, , 4] <- c(0.6, NA, 0.6, NA)
pA[, , 5] <- c(3, NA, 3, NA)
pA[, , 6] <- c(NA, NA, NA, NA)
pA[, , 7] <- c(NA, NA, 1, NA)
pA[, , 8] <- c(2, NA, 2, NA)

# Position priors for Phi
pP <- matrix(data = 0, nrow = ((nlags * ncol(pA)) + 1), ncol = ncol(pA))
pP[1:nrow(pA), 1:ncol(pA)] <-
  diag(x = 1, nrow = nrow(pA), ncol = ncol(pA))

# Confidence in the priors for Phi
x1 <- 
  matrix(data = NA, nrow = (nrow(y) - nlags), 
         ncol = (ncol(y) * nlags))
for (k in 1:nlags) {
  x1[, (ncol(y) * (k - 1) + 1):(ncol(y) * k)] <-
    y[(nlags - k + 1):(nrow(y) - k),]
}
x1 <- cbind(x1, 1)
colnames(x1) <- 
  c(paste(rep(colnames(y), nlags),
          "_L",
          sort(rep(seq(from = 1, to = nlags, by = 1), times = ncol(y)),
               decreasing = FALSE),
          sep = ""),
    "cons")
y1 <- y[(nlags + 1):nrow(y),]
ee <- matrix(data = NA, nrow = nrow(y1), ncol = ncol(y1))
for (i in 1:ncol(y1)) {
  xx <- cbind(x1[, seq(from = i, to = (ncol(x1) - 1), by = ncol(y1))], 1)
  yy <- matrix(data = y1[, i], ncol = 1)
  phi <- solve(t(xx) %*% xx, t(xx) %*% yy)
  ee[, i] <- yy - (xx %*% phi)
}
somega <- (t(ee) %*% ee) / nrow(ee)
lambda0 <- 0.2
lambda1 <- 1
lambda3 <- 100
v1 <- matrix(data = (1:nlags), nrow = nlags, ncol = 1)
v1 <- v1^((-2) * lambda1)
v2 <- matrix(data = diag(solve(diag(diag(somega)))), ncol = 1)
v3 <- kronecker(v1, v2)
v3 <- (lambda0^2) * rbind(v3, (lambda3^2))
v3 <- 1 / v3
pP_sig <- diag(x = 1, nrow = nrow(v3), ncol = nrow(v3))
diag(pP_sig) <- v3

# Confidence in long-run restriction priors
pR_sig <-
  array(data = 0,
        dim = c(((nlags * ncol(y)) + 1),
                ((nlags * ncol(y)) + 1),
                ncol(y)))
Ri <-
  cbind(kronecker(matrix(data = 1, nrow = 1, ncol = nlags),
                  matrix(data = c(1, 0), nrow = 1)),
        0)
pR_sig[, , 2] <- (t(Ri) %*% Ri) / 0.1

# Confidence in priors for D
kappa1 <- matrix(data = 2, nrow = 1, ncol = ncol(y))

# Set graphical parameters
par(cex.axis = 0.8, cex.main = 1, font.main = 1, family = "serif",
    mfrow = c(2, 2), mar = c(2, 2.2, 2, 1), las = 1)

# Estimate the parameters of the model
results1 <- 
  BH_SBVAR(y = y, nlags = nlags, pA = pA, pP = pP, pP_sig = pP_sig,
           pR_sig = pR_sig, kappa1 = kappa1, itr = itr, burn = burn,
           thin = thin, cri = cri)

fevd <- FEVD(results = results1, h = h, acc = acc, cri = cri)

# Plot impulse responses
varnames <- colnames(USLMData)[2:3]
shocknames <- c("Labor Demand","Labor Supply")
fevd_results <- 
  FEVD_Plots(results = fevd, varnames = varnames,
            shocknames = shocknames)